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Summary Table of Jags Posterior Estimates

Source: R/post_summ.R
post_summ.Rd

post_summ() takes a list output from run_mod() to summary table for parameter, antigen/antibody, and stratification combination. Defaults will produce every combination of antigen/antibody, parameters, and stratifications, unless otherwise specified. Antigen/antibody combinations and stratifications will vary by analysis. The antibody dynamic curve includes the following parameters:

  • y0 = baseline antibody concentration

  • y1 = peak antibody concentration

  • t1 = time to peak

  • r = shape parameter

  • alpha = decay rate

Usage

post_summ(
  data,
  iso = unique(data$Iso_type),
  param = unique(data$Parameter),
  strat = unique(data$Stratification)
)

Arguments

data

A list outputted from run_mod().

iso

Specify character string to produce tables of only a specific antigen/antibody combination, entered with quotes. Default outputs all antigen/antibody combinations.

param

Specify character string to produce tables of only a specific parameter, entered with quotes. Options include:

  • alpha = posterior estimate of decay rate

  • r = posterior estimate of shape parameter

  • t1 = posterior estimate of time to peak

  • y0 = posterior estimate of baseline antibody concentration

  • y1 = posterior estimate of peak antibody concentration

strat

Specify character string to produce tables of specific stratification entered in quotes.

Value

A data.frame summarizing estimate mean, standard deviation (SD), median, and quantiles (2.5%, 25.0%, 50.0%, 75.0%, 97.5%).

Author

Sam Schildhauer

Examples

post_summ(data = serodynamics::nepal_sees_jags_output)
#> # A tibble: 20 × 11
#>    Iso_type Parameter Stratification       Mean       SD  Median  `2.5%` `25.0%`
#>    <chr>    <chr>     <chr>               <dbl>    <dbl>   <dbl>   <dbl>   <dbl>
#>  1 HlyE_IgA alpha     paratyphi         0.00229  3.75e-3 1.27e-3 1.11e-4 5.79e-4
#>  2 HlyE_IgA alpha     typhi             0.00378  6.16e-3 2.17e-3 3.08e-4 1.12e-3
#>  3 HlyE_IgA shape     paratyphi         1.66     2.74e-1 1.61e+0 1.29e+0 1.47e+0
#>  4 HlyE_IgA shape     typhi             1.65     3.68e-1 1.57e+0 1.23e+0 1.42e+0
#>  5 HlyE_IgA t1        paratyphi         3.85     1.34e+0 3.70e+0 1.86e+0 2.92e+0
#>  6 HlyE_IgA t1        typhi             7.62     4.83e+0 6.67e+0 2.26e+0 4.61e+0
#>  7 HlyE_IgA y0        paratyphi         2.55     9.07e-1 2.41e+0 1.23e+0 1.87e+0
#>  8 HlyE_IgA y0        typhi             2.90     2.92e+0 2.25e+0 5.76e-1 1.52e+0
#>  9 HlyE_IgA y1        paratyphi      1024.       6.84e+3 1.54e+2 8.85e+0 5.45e+1
#> 10 HlyE_IgA y1        typhi          1726.       7.50e+3 2.69e+2 8.41e+0 9.10e+1
#> 11 HlyE_IgG alpha     paratyphi         0.00257  2.60e-3 1.74e-3 2.29e-4 8.76e-4
#> 12 HlyE_IgG alpha     typhi             0.00175  1.69e-3 1.22e-3 2.38e-4 7.06e-4
#> 13 HlyE_IgG shape     paratyphi         1.36     2.03e-1 1.32e+0 1.10e+0 1.19e+0
#> 14 HlyE_IgG shape     typhi             1.52     3.88e-1 1.42e+0 1.11e+0 1.27e+0
#> 15 HlyE_IgG t1        paratyphi         4.78     2.09e+0 4.37e+0 1.93e+0 3.30e+0
#> 16 HlyE_IgG t1        typhi             9.76     8.02e+0 7.67e+0 1.87e+0 4.59e+0
#> 17 HlyE_IgG y0        paratyphi         1.72     7.66e-1 1.54e+0 7.09e-1 1.19e+0
#> 18 HlyE_IgG y0        typhi             2.29     1.94e+0 1.82e+0 3.80e-1 1.09e+0
#> 19 HlyE_IgG y1        paratyphi       833.       2.13e+3 2.88e+2 1.82e+1 1.09e+2
#> 20 HlyE_IgG y1        typhi           402.       6.91e+2 2.21e+2 3.02e+1 1.07e+2
#> # ℹ 3 more variables: `50.0%` <dbl>, `75.0%` <dbl>, `97.5%` <dbl>